I do t really understand, what is the process or mechanism for when it is happy with something? At some point it “liked” the way the armor was coming together and refined it is small amounts only.
The neural network always tries to predict the final image, but the diffusion process takes the vector and shrinks it, then turns it into a distribution by adding Gaussian noise, so if the model makes a decision, it may not be the final one.
Edit: I thought you were concerned about the model changing decision; the model has a defined amount of steps it can take, and this affects the amount that the diffusion process can shrink the vector from the Unet (the neural network).
What is happening is that the noiser/earlier timesteps are responsible for low-frequency features, while the final timesteps are responsible for high-frequency features.
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I do t really understand, what is the process or mechanism for when it is happy with something? At some point it “liked” the way the armor was coming together and refined it is small amounts only.
The neural network always tries to predict the final image, but the diffusion process takes the vector and shrinks it, then turns it into a distribution by adding Gaussian noise, so if the model makes a decision, it may not be the final one.
Edit: I thought you were concerned about the model changing decision; the model has a defined amount of steps it can take, and this affects the amount that the diffusion process can shrink the vector from the Unet (the neural network).
What is happening is that the noiser/earlier timesteps are responsible for low-frequency features, while the final timesteps are responsible for high-frequency features.
https://dsp.stackexchange.com/questions/1637/what-does-frequ...